Many products and methods utilized in the financial and consumer lending industry rely on some underlying model of risk prediction. Risk prediction models are typically used in the underlying analysis of a decision to grant credit to a consumer or institution, and such models are typically embodied in a credit score or other metric. In the area of structured securities, risk models may be used for pricing the securities. Current risk models, however, have many shortcomings. For example, current pricing models for structured securities, such as mortgage-backed securities, collateralized debt obligations, etc., are typically based on sub-optimal and aged static measures of retail credit risk. Existing risk models for structured securities have many shortcomings at the product level, credit tier level, and the portfolio level. For example, existing generic risk models provide a flat prediction of probability of an event, such as probability of default (PD), over a fixed time window and generally do not get updated after the loan's origination. Furthermore, these risk models do not capture critical aspects of default risk for mortgages, such as loan type, annual percentage rate (APR), loan-to-value percentages (LTV), and other specific loan-level data. In the present securities industry, when individual cash streams (which are often sourced from consumer debt) are aggregated into a security, the consumer identifying information for each cash stream is typically lost. For example, for mortgage-backed securities, the borrower identification for each specific loan in the security is not available.
Therefore, there is a need for, among other things, systems and methods for creating enriched data and applying the enriched data to improve risk prediction across numerous financial and lending products and applications, including, but not limited to, mortgage-backed securities and consumer credit analysis. In the securities area, these is a need for, among other things, improved PD estimates of underlying assets and more pertinent discounting factors for the cash flows of the assets, which would lead to more precise valuations of the securities and the creation of trading opportunities. For many financial products and applications, there is a need for, among other things, more dynamic credit-related data. The present invention addresses these and other needs, as will become apparent from the following detailed description and accompanying drawings.